import numpy
from onnx import numpy_helper
from onnx.helper import make_graph, make_model, make_node, make_opsetid, make_tensor_value_info, set_model_props


def create_model():
    initializers = []
    nodes = []
    inputs = []
    outputs = []
    functions = []

    # opsets
    opsets = {"": 13}

    # initializers

    list_value = [
        -0.013732648454606533,
        -0.005861935671418905,
        0.06889285147190094,
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        0.016256270930171013,
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        0.1631188541650772,
        0.05412565544247627,
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        0.12607362866401672,
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        0.7441706657409668,
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        0.4033295810222626,
        -0.13447114825248718,
        0.04623159021139145,
        0.2380414456129074,
        -1.226112723350525,
        2.150630235671997,
        -1.702580213546753,
        0.5305419564247131,
        -0.06836353242397308,
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        0.7035881280899048,
        -0.8389442563056946,
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        1.2609282732009888,
        -1.0670661926269531,
        0.4142579436302185,
        0.04739700257778168,
        -0.3265092074871063,
        1.1873037815093994,
        -1.6817731857299805,
        0.9709527492523193,
        -0.09095840901136398,
        -0.12556785345077515,
        0.0835147574543953,
        -0.24109329283237457,
        0.032948240637779236,
        0.46304041147232056,
        -0.6594106554985046,
        0.349990576505661,
        -0.04113377630710602,
        0.016451245173811913,
        0.008994563482701778,
        -0.028321878984570503,
        -0.05336569994688034,
        0.16036668419837952,
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        0.07205374538898468,
        0.15965768694877625,
        -0.3389044404029846,
        0.21603335440158844,
        0.04029613360762596,
        -0.0813034325838089,
        0.1019665077328682,
        -0.4873599112033844,
        0.7873126268386841,
        -0.2951086163520813,
        -0.43754327297210693,
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        -0.2335057258605957,
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        -1.2137882709503174,
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        -0.730407178401947,
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    value = numpy.array(list_value, dtype=numpy.float32).reshape((64, 3, 7, 7))
    tensor = numpy_helper.from_array(value, name="onnx::Conv_501")

    initializers.append(tensor)

    list_value = [
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    value = numpy.array(list_value, dtype=numpy.float32)
    tensor = numpy_helper.from_array(value, name="onnx::Conv_502")

    initializers.append(tensor)

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    value = numpy.array(list_value, dtype=numpy.float32).reshape((64, 64, 1, 1))
    tensor = numpy_helper.from_array(value, name="onnx::Conv_504")

    initializers.append(tensor)

    list_value = [
        5.195802688598633,
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    ]
    value = numpy.array(list_value, dtype=numpy.float32)
    tensor = numpy_helper.from_array(value, name="onnx::Conv_505")

    initializers.append(tensor)

    # inputs

    inputs.append(make_tensor_value_info("input", 1, ["batch_size", 3, 32, 32]))

    # outputs

    outputs.append(make_tensor_value_info("/layer1/layer1.0/relu/Relu_output_0", 1, ["batch_size", 64, 8, 8]))

    # nodes

    node = make_node(
        "Conv",
        ["input", "onnx::Conv_501", "onnx::Conv_502"],
        ["/conv1/Conv_output_0"],
        name="/conv1/Conv",
        dilations=[1, 1],
        group=1,
        kernel_shape=[7, 7],
        pads=[3, 3, 3, 3],
        strides=[2, 2],
        domain="",
    )
    nodes.append(node)

    node = make_node("Relu", ["/conv1/Conv_output_0"], ["/relu/Relu_output_0"], name="/relu/Relu", domain="")
    nodes.append(node)

    node = make_node(
        "MaxPool",
        ["/relu/Relu_output_0"],
        ["/maxpool/MaxPool_output_0"],
        name="/maxpool/MaxPool",
        ceil_mode=0,
        kernel_shape=[3, 3],
        pads=[1, 1, 1, 1],
        strides=[2, 2],
        domain="",
    )
    nodes.append(node)

    node = make_node(
        "Conv",
        ["/maxpool/MaxPool_output_0", "onnx::Conv_504", "onnx::Conv_505"],
        ["/layer1/layer1.0/conv1/Conv_output_0"],
        name="/layer1/layer1.0/conv1/Conv",
        dilations=[1, 1],
        group=1,
        kernel_shape=[1, 1],
        pads=[0, 0, 0, 0],
        strides=[1, 1],
        domain="",
    )
    nodes.append(node)

    node = make_node(
        "Relu",
        ["/layer1/layer1.0/conv1/Conv_output_0"],
        ["/layer1/layer1.0/relu/Relu_output_0"],
        name="/layer1/layer1.0/relu/Relu",
        domain="",
    )
    nodes.append(node)

    # opsets
    opset_imports = [make_opsetid(domain, 1 if version is None else version) for domain, version in opsets.items()]

    # graph
    graph = make_graph(nodes, "torch_jit", inputs, outputs, initializers)
    # '7'

    onnx_model = make_model(graph, opset_imports=opset_imports, functions=functions)
    onnx_model.ir_version = 7
    onnx_model.producer_name = "pytorch"
    onnx_model.producer_version = ""
    onnx_model.domain = ""
    onnx_model.model_version = 0
    onnx_model.doc_string = ""
    set_model_props(onnx_model, {})

    return onnx_model
